Original Full Length ArticleGenetic perturbations that impair functional trait interactions lead to reduced bone strength and increased fragility in mice
Introduction
Understanding how genotype and phenotype are connected remains a major impediment to identifying the genes contributing to complex traits. This association is complicated in the skeletal system because the phenotype of clinical interest—fracture resistance—cannot be measured directly in living humans. Consequently, genetic studies must rely on surrogate traits that can be measured non-invasively and that correlate with fracture resistance properties. Most genetic studies have been conducted using bone mineral density (BMD), because this trait is used clinically to identify individuals with low bone mass that may have a higher risk of fracturing [1]. Other studies used morphological traits derived from engineering analysis [2], [3] or combinations of traits derived from principle components analysis [4], [5], [6]. Although many of these traits correlate with mechanical function (i.e., whole bone stiffness and strength), an important concern is that the adaptive nature of the skeletal system is not taken into consideration in genetic analyses when traits are used individually or combined for reasons unrelated to functional adaptations.
Functional adaptations may complicate the choice of phenotype used in genetic studies, because genetic variants affecting one trait are sometimes compensated by coordinated changes in other traits [7], [8], [9]. Because the skeletal system shows a particular pattern in the way traits are coordinately regulated (i.e., a network of trait interactions), individuals can achieve similar functional outcomes by assembling different sets of traits [10]. Consequently, individual traits, because they are coordinately regulated, are “moving targets” and may not be reliable indicators of function and fracture resistance (Fig. 1A). Thus, quantitative trait loci (QTLs) identified as regulating single traits or complex combinations of traits (e.g., BMD) may not necessarily regulate fracture resistance. These functional trait coadaptations may explain why there is often inconsistent overlap among the QTLs regulating individual traits and those regulating bone strength in mice [11] and humans [12].
We propose that efforts aimed at identifying genes regulating fracture resistance will benefit from targeting the biological processes that are directly responsible for establishing mechanical function. This changes the focus from individual gene–trait relationships to functional coadaptations [13]. Bone, like many physiological systems, uses a closed-loop feedback system to establish function, such that bone cells adjust traits in response to signals that convey how close the structure is to a functional end-state or set point [14]. Consequently, biological factors regulating aspects of this feedback system (e.g., set points, cellular responsiveness, compensation, hysteresis) are subject to genetic regulation [15]. Herein, we analyzed the femora of C57BL/6J–ChrA/J/NaJ Chromosome Substitution Strains (CSSs) to identify the chromosomes harboring genes that regulate bone mechanical function. Prior work confirmed that individual chromosome substitutions significantly altered individual bone traits [16], [17], [18]. Whole bone stiffness varies widely among inbred mouse strains [19]. Many genetic studies compared C57BL/6J (B6) and C3H/HeJ (C3H) strains to identify the genes responsible for the increased BMD of C3H mice [1]. In contrast, we chose to compare A/J and B6 strains, because 1) these two strains achieved similar skeletal functional outcomes (i.e., similar stiffness relative to body size) but in distinct ways [10] and 2) the degree of variation in bone robustness, and the associated functional trait interactions observed for crosses derived from A/J and B6 [10] are similar to that observed for human long bones [20], [21]. Therefore, this CSS panel allowed us to systemically interrogate the mouse genome by testing the hypothesis that some chromosome substitutions will show altered morphology and composition while coordinately adjusting these traits to establish normal function. In contrast, other substitutions will disrupt the ability of the system to properly coordinate traits leading to altered mechanical function. We also identified the biomechanical mechanisms explaining how system function was maintained or impaired in each strain, and then tested how each biomechanical strategy affected fracture resistance properties.
Section snippets
Husbandry
A panel of 16 week old male C57BL/6J–ChrA/J/NaJ chromosome substitution strains were obtained from the Jackson Laboratory (Bar Harbor, ME, USA) for all 19 autosomes and the X chromosome (n = 10 per strain). Male 16 week old C57BL/6J (B6) and A/J mice (n = 40 and 10, respectively) were also purchased from Jackson Laboratory. All mice received water and were fed a standard rodent diet (Purina Rodent Chow 5001; Purina Mills, Richmond, IN, USA) ad libitum. Mice were housed with a maximum of 5 mice per
Variation among CSSs
We surveyed B6, A/J and the 20 CSSs for a total of 13 traits including body size measurements (4 traits), whole bone mechanical properties (4 traits), femoral cross-sectional morphology (4 traits), and tissue mineral density (1 trait). On average across the suite of traits, 5 CSSs differed significantly from B6 (range = 1 to 10 CSSs), and all traits showed at least one CSS that differed significantly from B6 (p < 0.008) when examining unadjusted trait values (Table 1a, Table 1b, Table 1c). Body
Discussion
We tested the hypothesis that a systems analysis of C57BL/6J–ChrA/J/NaJ (CSS) mice would identify chromosomes harboring genes that regulate mechanical function. We identified seven substitutions that showed altered cross-sectional morphology or TMD, but coordinately adjusted these traits in a way that established a similar level of mechanical function as B6 (CSS-4, 5, 8, 9, 17, 18, 19). Six substitutions showed altered cross-sectional morphology or TMD but significantly reduced mechanical
Disclosures
All authors state that they have no conflicts of interest.
Acknowledgments
Research reported in this publication was supported by the National Institutes of Health under award numbers AR44927 and S10RR026336 to KJJ and RR12305 to JHN. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank Dr. Stephen Schlecht and Melissa Ramcharan for their assistance in data collection.
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